In 2026, data stands as the lifeblood of the modern finance function, transforming it from a traditional steward of numbers into a proactive engine of strategic insight and business value across industries—from manufacturing and retail to healthcare, energy, and professional services.
High-quality, accessible data—combined with advanced data science capabilities—enables CFOs to move beyond retrospective reporting toward real-time predictive analytics, anomaly detection, risk forecasting, supply chain optimisation, customer profitability modelling, and personalised decision-making.
As Deloitte's Finance Trends 2026 reveals, finance leaders are increasingly infusing technical talent into their teams, with data scientists and accountants collaborating to build organisational agility and drive enterprise-wide impact.
With 87% of CFOs viewing AI (powered by data science) as extremely or very important to finance operations this year, and 63% of finance teams having fully deployed and actively using AI solutions, the emphasis on robust data foundations has never been greater—addressing fragmentation, ensuring quality, and unlocking actionable intelligence amid economic volatility and competitive pressures.
This evolution is especially pronounced in Asia-Pacific, where CFOs prioritise revenue growth while accelerating AI for data analytics and forecasting (with 44% adoption reported in recent regional insights).
The global data science and predictive analytics market continues its rapid expansion, projected to reach US$71.34 billion by 2031, fuelled by cross-industry applications in risk management, operational efficiency, fraud prevention, and strategic forecasting.
Yet success hinges on overcoming legacy pitfalls: siloed datasets, poor governance, and ad hoc processes that undermine AI potential.
Data science empowers finance teams to clean and structure data as the bedrock for innovation, shifting the focus from manual tasks to high-value strategic contributions such as scenario modelling, cost optimisation, working capital management, and sustainable growth.
While the telco sector exemplifies data-intensive challenges—where Danny Ho serves as CFO for a major telecom company in Hong Kong—the convergence of data science and finance mirrors broader trends across all industries.
As 2026 unfolds, finance functions are undergoing a profound transformation, propelled by data science's ability to unlock insights from vast, complex datasets. This shift is not merely technological; it's cultural and strategic, positioning CFOs at the helm of innovation in dynamic markets like Hong Kong and beyond.
Drawing on global and regional insights, alongside Ho's practical perspectives as a seasoned finance leader, the narrative highlights how data science empowers CFOs across sectors to navigate legacy challenges, regulatory complexities, and rapid AI adoption.
Building data science muscle without the legacy drag
Ho emphasises tackling poor data foundations and ad hoc legacy reports head-on: "A common problem I see and have come across is poor data foundations and ad hoc legacy reports built over the years, which extensively rely on IT resources to maintain."
He asserts that this is "exacerbated by CEOs who lack understanding of the underlying problems yet demand an immediate AI world and CTOs who are pressured into delivering 'AI solutions' without resolving the underlying issues that continue to produce unusable data as current processes continue unabated."
Finance teams, familiar with processes and data interdependencies, are ideally positioned to lead digital overhauls—whether in inventory forecasting for manufacturers, revenue recognition for SaaS firms, or claims processing in insurance.
He advocates a structured upskilling roadmap blending internal training with external partnerships: "CFOs should prioritise a structured upskilling roadmap that blends internal development with strategic external partnerships."
"Provide training programs that equip existing accountants and analysts with essential skills in data tools and AI concepts, without requiring full data science degrees. Hire for adaptability rather than specialised credentials and collaborate with external advisors or platforms to fill gaps quickly." Danny Ho
This avoids slow implementation pitfalls, fosters AI trust, and turns data science into a core competency, aligning with regional trends where 64% of leaders are building technical skills.
As Jo Chanik from the Chinese University of Hong Kong notes, institutions are ramping up data scientist recruitment to leverage datasets for profit, a trend that has quadrupled the number of roles since 2008 and is replicating across Asia.
Claiming the CFO seat at the AI governance table
In Asia's patchwork of regulations, Ho urges CFOs to position themselves as central architects of AI governance, often doubling as CROs to collaborate with CIOs on data agendas.
"CFOs must position themselves as central architects of AI governance to safeguard compliance and strategic alignment," says Ho.
He also suggests establishing frameworks that emphasise transparency, explainability, and ethical use of AI. "This proactive stance not only mitigates risks in diverse regulations but also elevates the CFO as a strategic partner in enterprise-wide decisions," he continues.
This echoes Kobsak Duangdee, secretary general of the Thai Bankers’ Association (TBA), who, at the Finance Thailand 2025, stressed the need for robust governance and skilled workforces to revolutionise risk management through AI.
Similarly, Tan Su Shan at DBS highlights cross-functional data science teams operating under ethical frameworks such as PURE, ensuring purposeful AI in customer service.
Dive in iteratively: AI pilots over perfection paralysis
Ho suggests resisting that and beginning with low-stakes pilots, such as automating routine tasks, to validate use cases, build belief within your team and the wider company, and gather feedback.
He warns against hype with a real-world example: "A good example (or bad example if you may) that I have seen is an AI engine developed in-house by a mobile operator... Little or no usage post implementation, let alone uplift."
Instead, he advocates an iterative AI rollout that balances speed with accuracy in a fast-changing world, rather than waiting for a holistic plan.
Ho advocates an iterative AI rollout: "This has to be an iterative approach. Start small and observe—a pragmatic approach with built-in safeguards to manage emerging risks and build momentum.
"I would advocate resisting that and begin with low-stakes pilots, such as automating routine tasks, to validate use cases, build belief within your team and the wider company, and gather feedback." Danny Ho
Define clear outcomes to avoid such failures.
This pragmatic approach aligns with Stripe’s regional head & managing director for Southeast Asia, India, Greater China, and South Korea Sarita Singh, who notes that agentic AI's shift to "doing" tasks yields gains like faster expansion for CFOs. In APAC, where 44% of leaders use AI for analytics, iterative methods mitigate risks amid volatility.
Beyond ROI: Metrics that sell the tech vision to boards
Ho cautions against over-relying on ROI for AI: "ROI is a great tool... However, it can become challenging when you find that the number of assumptions and loose associations is being built in."
CFOs should adopt a multifaceted framework incorporating financial, operational, and strategic metrics. "I like to look at this as a percentage of turnover to enhance visibility and understanding to protect the future," says Ho.
Use a pillar framework: financial (cost savings), operational (error reduction), and strategic (competitive positioning). Metrics such as time-to-value, productivity hours saved, NPS boosts, and fraud accuracy provide a holistic justification.
Kelvin Cen, head of Southeast Asia at Bloomberg underscores the data's "lifeblood" role in generating market advantages via AI models, advocating high-quality infrastructure. J.P. Morgan Commercial Banking head for Asia Pacific, Kerwin Clayton echoes the embrace of tech for growth amid uncertainty.
Traditional AI wisdom: Fuelling real-time risk wins
Ho draws from traditional AI's successes: "Lessons from traditional AI underscore the importance of robust data quality, explainability, and integration."
He asserts that AI excels at pattern recognition and anomaly detection, so prioritise high-quality, diverse datasets to avoid biases and enhance predictive analytics.
"Avoid pitfalls of model adoption. Beware of model herding and systemic risks by emphasising governance and auditability. Build explainable systems that maintain trust." Danny Ho
Prioritise pattern recognition in scenarios with large datasets, such as credit monitoring for headcount savings in telco or banking, fraud prevention in retail, and predictive maintenance in energy.
Avoid biases, model herding, and pitfalls in non-standard data; emphasise governance for trust. This builds on AI's widespread adoption, enabling predictive insights. Collectively, these insights from Ho and peers paint data science as the engine propelling finance toward agility and foresight.
For CFOs across industries, this means harnessing data for strategic growth—monetising data assets, optimising operations, and personalising stakeholder experiences in a data-saturated Asia and beyond, turning disruptions into sustained value.
